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{
"name": "openrouter",
"description": "Interacts with the OpenRouter API.",
"version": "0.0.0-2025.11.28",
"author": {
"name": "Tim Green",
"email": "rawveg@gmail.com"
},
"skills": [
"./skills/openrouter"
]
}

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# openrouter
Interacts with the OpenRouter API.

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---
name: openrouter
description: OpenRouter API - Unified access to 400+ AI models through one API
---
# OpenRouter Skill
Comprehensive assistance with OpenRouter API development, providing unified access to hundreds of AI models through a single endpoint with intelligent routing, automatic fallbacks, and standardized interfaces.
## When to Use This Skill
This skill should be triggered when:
- Making API calls to multiple AI model providers through a unified interface
- Implementing model fallback strategies or auto-routing
- Working with OpenAI-compatible SDKs but targeting multiple providers
- Configuring advanced sampling parameters (temperature, top_p, penalties)
- Setting up streaming responses or structured JSON outputs
- Comparing costs across different AI models
- Building applications that need automatic provider failover
- Implementing function/tool calling across different models
- Questions about OpenRouter-specific features (routing, fallbacks, zero completion insurance)
## Quick Reference
### Basic Chat Completion (Python)
```python
from openai import OpenAI
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key="<OPENROUTER_API_KEY>",
)
completion = client.chat.completions.create(
model="openai/gpt-4o",
messages=[{"role": "user", "content": "What is the meaning of life?"}]
)
print(completion.choices[0].message.content)
```
### Basic Chat Completion (JavaScript/TypeScript)
```typescript
import OpenAI from 'openai';
const openai = new OpenAI({
baseURL: 'https://openrouter.ai/api/v1',
apiKey: '<OPENROUTER_API_KEY>',
});
const completion = await openai.chat.completions.create({
model: 'openai/gpt-4o',
messages: [{"role": 'user', "content": 'What is the meaning of life?'}],
});
console.log(completion.choices[0].message);
```
### cURL Request
```bash
curl https://openrouter.ai/api/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENROUTER_API_KEY" \
-d '{
"model": "openai/gpt-4o",
"messages": [{"role": "user", "content": "What is the meaning of life?"}]
}'
```
### Model Fallback Configuration (Python)
```python
completion = client.chat.completions.create(
model="openai/gpt-4o",
extra_body={
"models": ["anthropic/claude-3.5-sonnet", "gryphe/mythomax-l2-13b"],
},
messages=[{"role": "user", "content": "Your prompt here"}]
)
```
### Model Fallback Configuration (TypeScript)
```typescript
const completion = await client.chat.completions.create({
model: 'openai/gpt-4o',
models: ['anthropic/claude-3.5-sonnet', 'gryphe/mythomax-l2-13b'],
messages: [{ role: 'user', content: 'Your prompt here' }],
});
```
### Auto Router (Dynamic Model Selection)
```python
completion = client.chat.completions.create(
model="openrouter/auto", # Automatically selects best model for the prompt
messages=[{"role": "user", "content": "Your prompt here"}]
)
```
### Advanced Parameters Example
```python
completion = client.chat.completions.create(
model="openai/gpt-4o",
messages=[{"role": "user", "content": "Write a creative story"}],
temperature=0.8, # Higher for creativity (0.0-2.0)
max_tokens=500, # Limit response length
top_p=0.9, # Nucleus sampling (0.0-1.0)
frequency_penalty=0.5, # Reduce repetition (-2.0-2.0)
presence_penalty=0.3 # Encourage topic diversity (-2.0-2.0)
)
```
### Streaming Response
```python
stream = client.chat.completions.create(
model="openai/gpt-4o",
messages=[{"role": "user", "content": "Tell me a story"}],
stream=True
)
for chunk in stream:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end='')
```
### JSON Mode (Structured Output)
```python
completion = client.chat.completions.create(
model="openai/gpt-4o",
messages=[{
"role": "user",
"content": "Extract person's name, age, and city from: John is 30 and lives in NYC"
}],
response_format={"type": "json_object"}
)
```
### Deterministic Output with Seed
```python
completion = client.chat.completions.create(
model="openai/gpt-4o",
messages=[{"role": "user", "content": "Generate a random number"}],
seed=42, # Same seed = same output (when supported)
temperature=0.0 # Deterministic sampling
)
```
## Key Concepts
### Model Routing
OpenRouter provides intelligent routing capabilities:
- **Auto Router** (`openrouter/auto`): Automatically selects the best model based on your prompt using NotDiamond
- **Fallback Models**: Specify multiple models that automatically retry if primary fails
- **Provider Routing**: Automatically routes across providers for reliability
### Authentication
- Uses Bearer token authentication with API keys
- API keys can be managed programmatically
- Compatible with OpenAI SDK authentication patterns
### Model Naming Convention
Models use the format `provider/model-name`:
- `openai/gpt-4o` - OpenAI's GPT-4 Optimized
- `anthropic/claude-3.5-sonnet` - Anthropic's Claude 3.5 Sonnet
- `google/gemini-2.0-flash-exp:free` - Google's free Gemini model
- `openrouter/auto` - Auto-routing system
### Sampling Parameters
**Temperature** (0.0-2.0, default: 1.0)
- Lower = more predictable, focused responses
- Higher = more creative, diverse responses
- Use low (0.0-0.3) for factual tasks, high (0.8-1.5) for creative work
**Top P** (0.0-1.0, default: 1.0)
- Limits choices to percentage of likely tokens
- Dynamic filtering of improbable options
- Balance between consistency and variety
**Frequency/Presence Penalties** (-2.0-2.0, default: 0.0)
- Frequency: Discourages repeating tokens proportional to use
- Presence: Simpler penalty not scaled by count
- Positive values reduce repetition, negative encourage reuse
**Max Tokens** (integer)
- Sets maximum response length
- Cannot exceed context length minus prompt length
- Use to control costs and enforce concise replies
### Response Formats
- **Standard JSON**: Default chat completion format
- **Streaming**: Server-Sent Events (SSE) with `stream: true`
- **JSON Mode**: Guaranteed valid JSON with `response_format: {"type": "json_object"}`
- **Structured Outputs**: Schema-validated JSON responses
### Advanced Features
- **Tool/Function Calling**: Connect models to external APIs
- **Multimodal Inputs**: Support for images, PDFs, audio
- **Prompt Caching**: Reduce costs for repeated prompts
- **Web Search Integration**: Enhanced responses with web data
- **Zero Completion Insurance**: Protection against failed responses
- **Logprobs**: Access token probabilities for confidence analysis
## Reference Files
This skill includes comprehensive documentation in `references/`:
- **llms-full.md** - Complete list of available models with metadata
- **llms-small.md** - Curated subset of popular models
- **llms.md** - Standard model listings
Use `view` to read specific reference files when detailed model information is needed.
## Working with This Skill
### For Beginners
1. Start with basic chat completion examples (Python/JavaScript/cURL above)
2. Use the standard OpenAI SDK for easy integration
3. Try simple model names like `openai/gpt-4o` or `anthropic/claude-3.5-sonnet`
4. Keep parameters simple initially (just model and messages)
### For Intermediate Users
1. Implement model fallback arrays for reliability
2. Experiment with sampling parameters (temperature, top_p)
3. Use streaming for better UX in conversational apps
4. Try `openrouter/auto` for automatic model selection
5. Implement JSON mode for structured data extraction
### For Advanced Users
1. Fine-tune multiple sampling parameters together
2. Implement custom routing logic with fallback chains
3. Use logprobs for confidence scoring
4. Leverage tool/function calling capabilities
5. Optimize costs by selecting appropriate models per task
6. Implement prompt caching strategies
7. Use seed parameter for reproducible testing
## Common Patterns
### Error Handling with Fallbacks
```python
try:
completion = client.chat.completions.create(
model="openai/gpt-4o",
extra_body={
"models": [
"anthropic/claude-3.5-sonnet",
"google/gemini-2.0-flash-exp:free"
]
},
messages=[{"role": "user", "content": "Your prompt"}]
)
except Exception as e:
print(f"All models failed: {e}")
```
### Cost-Optimized Routing
```python
# Use cheaper models for simple tasks
simple_completion = client.chat.completions.create(
model="google/gemini-2.0-flash-exp:free",
messages=[{"role": "user", "content": "Simple question"}]
)
# Use premium models for complex tasks
complex_completion = client.chat.completions.create(
model="openai/o1",
messages=[{"role": "user", "content": "Complex reasoning task"}]
)
```
### Context-Aware Temperature
```python
# Low temperature for factual responses
factual = client.chat.completions.create(
model="openai/gpt-4o",
temperature=0.2,
messages=[{"role": "user", "content": "What is the capital of France?"}]
)
# High temperature for creative content
creative = client.chat.completions.create(
model="openai/gpt-4o",
temperature=1.2,
messages=[{"role": "user", "content": "Write a unique story opening"}]
)
```
## Resources
### Official Documentation
- API Reference: https://openrouter.ai/docs/api-reference/overview
- Quickstart Guide: https://openrouter.ai/docs/quickstart
- Model List: https://openrouter.ai/docs/models
- Parameters Guide: https://openrouter.ai/docs/api-reference/parameters
### Key Endpoints
- Chat Completions: `POST https://openrouter.ai/api/v1/chat/completions`
- List Models: `GET https://openrouter.ai/api/v1/models`
- Generation Info: `GET https://openrouter.ai/api/v1/generation`
## Notes
- OpenRouter normalizes API schemas across all providers
- Uses OpenAI-compatible API format for easy migration
- Automatic provider fallback if models are rate-limited or down
- Pricing based on actual model used (important for fallbacks)
- Response includes metadata about which model processed the request
- All models support streaming via Server-Sent Events
- Compatible with popular frameworks (LangChain, Vercel AI SDK, etc.)
## Best Practices
1. **Always implement fallbacks** for production applications
2. **Use appropriate temperature** based on task type (low for factual, high for creative)
3. **Set max_tokens** to control costs and response length
4. **Enable streaming** for better user experience in chat applications
5. **Use JSON mode** when you need guaranteed structured output
6. **Test with seed parameter** for reproducible results during development
7. **Monitor costs** by selecting appropriate models per task
8. **Use auto-routing** when unsure which model performs best
9. **Implement proper error handling** for rate limits and failures
10. **Cache prompts** for repeated requests to reduce costs

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{
"name": "openrouter",
"description": "Interacts with the OpenRouter API.",
"version": "1.0.0",
"author": {
"name": "Tim Green",
"email": "rawveg@gmail.com"
},
"homepage": "https://github.com/rawveg/claude-skills-marketplace",
"repository": "https://github.com/rawveg/claude-skills-marketplace",
"license": "MIT",
"keywords": ["openrouter", "Claude Code"],
"category": "productivity",
"strict": false
}

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# Openrouter Documentation Index
## Categories
### Other
**File:** `other.md`
**Pages:** 1

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